nep-ets New Economics Papers
on Econometric Time Series
Issue of 2015‒10‒25
eight papers chosen by
Yong Yin
SUNY at Buffalo

  1. A SPECTRAL EM ALGORITHM FOR DYNAMIC FACTOR MODELS By Gabriele Fiorentini; Alessandro Galesi; Enrique Sentana
  2. Multifractal Flexibly Detrended Fluctuation Analysis By Rafal Rak; Pawel Zi\k{e}ba
  4. Orthogonal Series Estimation in Nonlinear Cointegrating Models with Endogeneity By Biqing Cai; Chaohua Dong; Jiti Gao
  5. Real-Time Forecasting with a Large, Mixed Frequency, Bayesian VAR By McCracken, Michael W.; Owyang, Michael T.; Sekhposyan, Tatevik
  6. Testing for a Structural Break in Dynamic Panel Data Models with Common Factors By Huanjun Zhu; Vasilis Sarafidis; Mervyn Silvapulle; Jiti Gao
  7. Testing for Spacial Lag and Spatial Error Dependence in a Fixed Effects Panel Data Model Using Double Length Artificial Regressions By Badi H. Baltagi; Long Liu
  8. Tests for sphericity in multivariate garch models By Francq, Christian; Jiménez Gamero, Maria Dolores; Meintanis, Simos

  1. By: Gabriele Fiorentini (Università di Firenze); Alessandro Galesi (CEMFI, Centro de Estudios Monetarios y Financieros); Enrique Sentana (CEMFI, Centro de Estudios Monetarios y Financieros)
    Abstract: We introduce a frequency domain version of the EM algorithm for general dynamic factor models. We consider both AR and ARMA processes, for which we develop iterative indirect inference procedures analogous to the algorithms in Hannan (1969). Although our proposed procedure allows researchers to estimate such models by maximum likelihood with many series even without good initial values, we recommend switching to a gradient method that uses the EM principle to swiftly compute frequency domain analytical scores near the optimum. We successfully employ our algorithm to construct an index that captures the common movements of US sectoral employment growth rates.
    Keywords: Indirect inference, Kalman filter, sectoral employment, spectral maximum likelihood, Wiener-Kolmogorov filter.
    JEL: C32 C38 C51
    Date: 2014–12
  2. By: Rafal Rak; Pawel Zi\k{e}ba
    Abstract: Multifractal time series analysis is a approach that shows the possible complexity of the system. Nowadays, one of the most popular and the best methods for determining multifractal characteristics is Multifractal Detrended Fluctuation Analysis (MFDFA). However, it has some drawback. One of its core elements is detrending of the series. In the classical MFDFA a trend is estimated by fitting a polynomial of degree $m$ where $m=const$. We propose that the degree $m$ of a polynomial was not constant ($m\neq const$) and its selection was ruled by an established criterion. Taking into account the above amendment, we examine the multifractal spectra both for artificial and real-world mono- and the multifractal time series. Unlike classical MFDFA method, obtained singularity spectra almost perfectly reflects the theoretical results and for real time series we observe a significant right side shift of the spectrum.
    Date: 2015–10
  3. By: Manuel Arellano (CEMFI, Centro de Estudios Monetarios y Financieros); Stéphane Bonhomme (University of Chicago)
    Abstract: We introduce a class of quantile regression estimators for short panels. Our framework covers static and dynamic autoregressive models, models with general predetermined regressors, and models with multiple individual effects. We use quantile regression as a flexible tool to model the relationships between outcomes, covariates, and heterogeneity. We develop an iterative simulation-based approach for estimation, which exploits the computational simplicity of ordinary quantile regression in each iteration step. Finally, an application to measure the effect of smoking during pregnancy on children’s birthweights completes the paper.
    Keywords: Cokurtosis, coskewness, indirect inference, Kuhn-Tucker test, momentum strategies, non-linear dependence, short-term reversals, supremum test, underidentified parameters.
    JEL: C23
    Date: 2015–07
  4. By: Biqing Cai; Chaohua Dong; Jiti Gao
    Abstract: This paper proposes a new statistic to conduct cross-sectional independence test for the residuals involved in a parametric panel data model. The proposed test statistic, which is called linear spectral statistic (LSS), is established based on the characteristic function of the empirical spectral distribution (ESD) of the sample correlation matrix of the residuals. The main advantage of the proposed test statistic is that it can capture nonlinear cross-sectional dependence. Asymptotic theory for a general class of linear spectral statistics is established, as the cross-sectional dimension N and time length T go to infinity proportionally. This type of statistics covers many classical statistics, including the bias-corrected Lagrange Multiplier (LM) test statistic and the likelihood ratio test statistic. Furthermore, the power under a local alternative hypothesis is analyzed and the asymptotic distribution of the proposed statistic under this local hypothesis is also established. Finite sample performance shows that the proposed test statistic works well numerically in each individual case and it can also distinguish some dependent but uncorrelated structures, for example, nonlinear MA(1) models and multiple ARCH(1) models.
    Keywords: Characteristic function, cross–sectional independence, empirical spectral distribution, linear panel data models, Marcenko-Pastur Law
    JEL: C12 C21 C22
    Date: 2015
  5. By: McCracken, Michael W. (Federal Reserve Bank of St. Louis); Owyang, Michael T. (Federal Reserve Bank of St. Louis); Sekhposyan, Tatevik (Texas A&M University)
    Abstract: We assess point and density forecasts from a mixed-frequency vector autoregression (VAR) to obtain intra-quarter forecasts of output growth as new information becomes available. The econometric model is specified at the lowest sampling frequency; high frequency observations are treated as different economic series occurring at the low frequency. We impose restrictions on the VAR to account explicitly for the temporal ordering of the data releases. Because this type of data stacking results in a high-dimensional system, we rely on Bayesian shrinkage to mitigate parameter proliferation. The relative performance of the model is compared to forecasts from various time-series models and the Survey of Professional Forecaster's. We further illustrate the possible usefulness of our proposed VAR for causal analysis.
    Keywords: Vector autoregression; Blocking model; Stacked vector autoregression; Mixed-frequency estimation; Bayesian methods; Nowcasting; Forecasting
    JEL: C22 C52 C53
    Date: 2015–10–08
  6. By: Huanjun Zhu; Vasilis Sarafidis; Mervyn Silvapulle; Jiti Gao
    Abstract: This paper develops a method for testing for the presence of a single structural break in panel data models with unobserved heterogeneity represented by a factor error structure. The common factor approach is an appealing way to capture the effect of unobserved variables, such as skills and innate ability in studies of returns to education, common shocks and cross-sectional dependence in models of economic growth, law enforcement acts and public attitudes towards crime in statistical modelling of criminal behaviour. Ignoring these variables may result in inconsistent parameter estimates and invalid inferences. We focus on the case where the time frequency of the data may be yearly and thereby the number of time series observations is small, even if the sample covers a rather long period of time. We develop a Distance type statistic based on a Method of Moments estimator that allows for unobserved common factors. Existing structural break tests proposed in the literature are not valid under these circumstances. The asymptotic properties of the test statistic are established for both known and unknown breakpoints. In our simulation study, the method performed well, both in terms of size and power, as well as in terms of successfully locating the time at which the break occurred. The method is illustrated using data from a large sample of banking institutions, providing empirical evidence on the well-known Gibrat's `Law'.
    Keywords: Method of moments, unobserved heterogeneity, break-point detection, fixed T asymptotics
    JEL: C11 C15 C18
    Date: 2015
  7. By: Badi H. Baltagi (Center for Policy Research, Maxwell School, Syracuse University, 426 Eggers Hall, Syracuse, NY 13244); Long Liu (Department of Economics, College of Business, University of Texas at San Antonio, One UTSA Circle, TX 78249-0633)
    Abstract: This paper revisits the joint and conditional Lagrange Multiplier tests derived by Debarsy and Ertur (2010) for a fixed effects spatial lag regression model with spatial auto-regressive error, and derives these tests using artificial Double Length Regressions (DLR). These DLR tests and their corresponding LM tests are compared using an empirical example and a Monte Carlo simulation.
    Keywords: Double Length Regresson; Spatial Lag Dependence; Spatial Error Dependence; Artificial Regressions; Panel Data; Fixed Effects
    JEL: C12 R15
    Date: 2015–09
  8. By: Francq, Christian; Jiménez Gamero, Maria Dolores; Meintanis, Simos
    Abstract: Tests for spherical symmetry of the innovation distribution are proposed in multivariate GARCH models. The new tests are of Kolmogorov--Smirnov and Cram\'er--von Mises--type and make use of the common geometry underlying the characteristic function of any spherically symmetric distribution. The asymptotic null distribution of the test statistics as well as the consistency of the tests is investigated under general conditions. It is shown that both the finite sample and the asymptotic null distribution depend on the unknown distribution of the Euclidean norm of the innovations. Therefore a conditional Monte Carlo procedure is used to actually carry out the tests. The validity of this resampling scheme is formally justified. Results on the behavior of the test in finite--samples are included, as well as an application on financial data.
    Keywords: Extended CCC-GARCH; Spherical symmetry; Empirical characteristic function; Conditional Monte Carlo test
    JEL: C12 C15 C32 C58
    Date: 2015–09

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